SoroushVahidi/scidocs huggingface dataset:
Transition-based Dependency DAG Parsing Using Dynamic Oracles
(2015)

In most of the dependency parsing studies, dependency relations within a sentence are often presented as a tree structure. Whilst the tree structure is sufficient to represent the surface relations, deep dependencies which may result to multi-headed relations require more general dependency structures, namely Directed Acyclic Graphs (DAGs). This study proposes a new dependency DAG parsing approach which uses a dynamic oracle within a shift-reduce transitionbased parsing framework. Although there is still room for improvement on performance with more feature engineering, we already obtain competitive performances compared to static oracles as a result of our initial experiments conducted on the ITU-METU-Sabancı Turkish Treebank (IMST).
Exploring Session Context using Distributed Representations of Queries and Reformulations
(2015)

Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "San Francisco" -- "San Francisco 49ers" is semantically similar to "Detroit" -- "Detroit Lions". Likewise, "London" -- "things to do in London" and "New York" -- "New York tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" -- "new movies" and "york" -- "New York", however, are clearly different despite the lexical similarities in the two reformulations. In this paper, we study the distributed representation of queries learnt by deep neural network models, such as the Convolutional Latent Semantic Model, and show that they can be used to represent query reformulations as vectors. These reformulation vectors exhibit favourable properties such as mapping semantically and syntactically similar query changes closer in the embedding space. Our work is motivated by the success of continuous space language models in capturing relationships between words and their meanings using offset vectors. We demonstrate a way to extend the same intuition to represent query reformulations. Furthermore, we show that the distributed representations of queries and reformulations are both useful for modelling session context for query prediction tasks, such as for query auto-completion (QAC) ranking. Our empirical study demonstrates that short-term (session) history context features based on these two representations improves the mean reciprocal rank (MRR) for the QAC ranking task by more than 10% over a supervised ranker baseline. Our results also show that by using features based on both these representations together we achieve a better performance, than either of them individually.
E-ethical leadership for virtual project teams
(2009)

This paper presents a review of current literature on ethical theories as they relate to ethical leadership in the virtual business environment (e-ethics) and virtual project leadership. Ethical theories are reviewed in relation to virtual project management, such as participative management, Theory Y, and its relationship to utilitarianism; Kantian ethics, motivation, and trust; communitarian ethics, ethic of care and egalitarianism; Stakeholder Theory; and the use of political tactics. Challenges to e-ethical leadership are presented and responses to these issues discussed. The conclusion presents four propositions for future research. The purpose of this paper is to identify secondary literature on e-ethics and how this new area of business ethics may affect the leaders of virtual project teams. 2008 Elsevier Ltd and IPMA. All rights reserved.
1 . RELATED WORK 1 . 1 3 D Reconstruction
(2016)

Creating and animating realistic 3D human faces is an important element of virtual reality, video games, and other areas that involve interactive 3D graphics. In this paper, we propose a system to generate photorealistic 3D blendshape-based face models automatically using only a single consumer RGB-D sensor. The capture and processing requires no artistic expertise to operate, takes 15 seconds to capture and generate a single facial expression, and approximately 1 minute of processing time per expression to transform it into a blendshape model. Our main contributions include a complete end-to-end pipeline for capturing and generating photorealistic blendshape models automatically and a registration method that solves dense correspondences between two face scans by utilizing facial landmarks detection and optical flows. We demonstrate the effectiveness of the proposed method by capturing different human subjects with a variety of sensors and puppeteering their 3D faces with real-time facial performance retargeting. The rapid nature of our method allows for just-in-time construction of a digital face. To that end, we also integrated our pipeline with a virtual reality facial performance capture system that allows dynamic embodiment of the generated faces despite partial occlusion of the user’s real face by the head-mounted display.
Non-linear robust control for inverted-pendulum 2D walking
(2015)

We present an approach to high-level control for bipedal walking exemplified with a 2D point-mass inextensible-legs inverted-pendulum model. Balance control authority here is only from step position and trailing-leg push-off, both of which are bounded to reflect actuator limits. The controller is defined implicitly as the solution of an optimization problem. The optimization robustly avoids falling for given bounded disturbances and errors and, given that, minimizes the number of steps to reach a given target speed. The optimization can be computed in advance and stored for interpolated real-time use online. The general form of the resulting optimized controller suggests a few simple principles for regulating walking speed: 1) The robot should take bigger steps when speeding up and should also take bigger steps when slowing down 2) push-off is useful for regulating small changes in speed, but it is fully saturated or inactive for larger changes in speed. While the numerically optimized model is simple, the approach should be applicable to, and we plan to use it for, control of bipedal robots in 3D with many degrees of freedom.
On mining cross-graph quasi-cliques
(2005)

Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.
Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs
(2005)

We consider wireless LANs such as IEEE 802.11 operating in the unlicensed radio spectrum. While their nominal bit rates have increased considerably, the MAC layer remains practically unchanged despite much research effort spent on improving its performance. We observe that most proposals for tuning the access method focus on a single aspect and disregard others. Our objective is to define an access method optimized for throughput and fairness, able to dynamically adapt to physical channel conditions, to operate near optimum for a wide range of error rates, and to provide equal time shares when hosts use different bit rates.We propose a novel access method derived from 802.11 DCF [2] (Distributed Coordination Function) in which all hosts use similar values of the contention window CW to benefit from good short-term access fairness. We call our method Idle Sense, because each host observes the mean number of idle slots between transmission attempts to dynamically control its contention window. Unlike other proposals, Idle Sense enables each host to estimate its frame error rate, which can be used for switching to the right bit rate. We present simulations showing how the method leads to high throughput, low collision overhead, and low delay. The method also features fast reactivity and time-fair channel allocation.
Path Planning with Real Time Obstacle Avoidance
(2013)

One of the most important areas of research on mobile robots is that of their moving from one point in a space to the other and that too keeping aloof from the different objects in their path i. e. real time obstacle detection. This is the basic problem of path planning through a continuous domain. For this a large number of algorithms have been proposed of which only a few are really good as far as local and global path planning are concerned as to some extent a trade of has to be made between the efficiency of the algorithm and its accuracy. In this project an integrated approach for both local as well as global path planning of a robot has been proposed. The primary algorithm that has been used for path planning is the artificial Potential field approach [1] and a* search algorithm has been used for finding the most optimum path for the robot. Obstacle detection for collision avoidance (a high level planning problem) can be effectively done by doing complex robot operations in real time and distributing the whole problem between different control levels. This project proposes the artificial potential field algorithm not only for static but also for moving obstacles using real time potential field values by creating sub-goals which eventually lead to the main goal of the most optimal complete path found by the A* search algorithm. Apart from these scan line and convex hull techniques have been used to improve the robustness of the algorithm. To some extent the shape and size of a robot has also been taken into consideration. The effectiveness of these algorithms has been
Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications
(2017)

Nowadays, there is an ever-increasing migration of people to urban areas. Health care service is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystems for people. In such a transformation, millions of homes are being equipped with smart devices (e.g., smart meters, sensors, and so on), which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using a shower/bath. This paper addresses the need to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this paper uses the U.K. Domestic Appliance Level Electricity data set—time series data of power consumption collected from 2012 to 2015 with the time resolution of 6 s for five houses with 109 appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 h, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented in detail in this paper along with the accuracy of short- and long-term predictions.
Blockchain-based sharing services : What blockchain technology can contribute to smart cities
(2017)

Background: The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word “smart” and benefits from innovative applications of new kinds of information and communications technology to support communal sharing. Methods: By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective. Results: Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services. Conclusions: This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area.
Rényi Divergence and Kullback-Leibler Divergence
(2014)

Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is related to Shannon's entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as Kullback-Leibler divergence, and depends on a parameter that is called its order. In particular, the Rényi divergence of order 1 equals the Kullback-Leibler divergence. We review and extend the most important properties of Rényi divergence and Kullback- Leibler divergence, including convexity, continuity, limits of σ-algebras, and the relation of the special order 0 to the Gaussian dichotomy and contiguity. We also show how to generalize the Pythagorean inequality to orders different from 1, and we extend the known equivalence between channel capacity and minimax redundancy to continuous channel inputs (for all orders) and present several other minimax results.
Advanced Metamorphic Techniques in Computer Viruses
(2007)

Nowadays viruses use polymorphic techniques to mutate their code on each replication, thus evading detection by antiviruses. However detection by emulation can defeat simpl e polymorphism: thus metamorphic techniques are used which thoro ughly change the viral code, even after decryption. We briefly deta il this evolution of virus protection techniques against detectio n and then study the METAPHOR virus, today’s most advanced metamorphic virus. Keywords—Computer virus, Viral mutation, Polymorphism, Metamorphism, MetaPHOR, Virus history, Obfuscation, Viral gen etic techniques
Split learning for health: Distributed deep learning without sharing raw patient data
(2018)

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.
Computer proficiency questionnaire: assessing low and high computer proficient seniors.
(2015)

PURPOSE OF THE STUDY
Computers and the Internet have the potential to enrich the lives of seniors and aid in the performance of important tasks required for independent living. A prerequisite for reaping these benefits is having the skills needed to use these systems, which is highly dependent on proper training. One prerequisite for efficient and effective training is being able to gauge current levels of proficiency. We developed a new measure (the Computer Proficiency Questionnaire, or CPQ) to measure computer proficiency in the domains of computer basics, printing, communication, Internet, calendaring software, and multimedia use. Our aim was to develop a measure appropriate for individuals with a wide range of proficiencies from noncomputer users to extremely skilled users.


DESIGN AND METHODS
To assess the reliability and validity of the CPQ, a diverse sample of older adults, including 276 older adults with no or minimal computer experience, was recruited and asked to complete the CPQ.


RESULTS
The CPQ demonstrated excellent reliability (Cronbach's α = .98), with subscale reliabilities ranging from .86 to .97. Age, computer use, and general technology use all predicted CPQ scores. Factor analysis revealed three main factors of proficiency related to Internet and e-mail use; communication and calendaring; and computer basics. Based on our findings, we also developed a short-form CPQ (CPQ-12) with similar properties but 21 fewer questions.


IMPLICATIONS
The CPQ and CPQ-12 are useful tools to gauge computer proficiency for training and research purposes, even among low computer proficient older adults.
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods
(2017)

Despite the great developments in information technology, particularly the Internet, computer networks, global information exchange, and its positive impact in all areas of daily life, it has also contributed to the development of penetration and intrusion which forms a high risk to the security of information organizations, government agencies, and causes large economic losses. There are many techniques designed for protection such as firewall and intrusion detection systems (IDS). IDS is a set of software and/or hardware techniques used to detect hacker's activities in computer systems. Two types of anomalies are used in IDS to detect intrusive activities different from normal user behavior. Misuse relies on the knowledge base that contains all known attack techniques and intrusion is discovered through research in this knowledge base. Artificial intelligence techniques have been introduced to improve the performance of these systems. The importance of IDS is to identify unauthorized access attempting to compromise confidentiality, integrity or availability of the computer network. This paper investigates the Intrusion Detection (ID) problem using three machine learning algorithms namely, BayesNet algorithm, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The algorithms are applied on a real, Management Information Based (MIB) dataset that is collected from real life environment. To enhance the detection process accuracy, a set of feature selection approaches is used; Infogain (IG), ReleifF (RF), and Genetic Search (GS). Our experiments show that the three feature selection methods have enhanced the classification performance. GS with bayesNet, MLP and SVM give high accuracy rates, more specifically the BayesNet with the GS accuracy rate is 99.9%.
IGCTs vs. IGBTs for circuit breakers in advanced ship electrical systems
(2009)

Solid-State Circuit Breakers (SSCBs) are being considered for several of the new Navy ships' power distribution platforms. This is because SSCBs offer many advantages to electrical systems that conventional Electro-Mechanical Circuit Breakers (EMCBs) cannot. These advantages include much faster interruption of overcurrents, based on lower current thresholds and/or di/dt rate of current rise; reduced energy flowing into the electrical system during faults; and reduced system ringing during fault interruptions. Additional advantages of SSCBs are the elimination of electrical arcing, which is particularly useful for DC and higher voltage distribution systems and systems in environments with volatile gases or explosives; reduced acoustic noise and Electro-Magnetic Interference (EMI) levels during circuit breaker switching; and reduced maintenance and lifecycle costs during the typical 40-year lifetime of the ship. This paper describes the advantages and disadvantages of SSCBs using IGCTs, SSCBs using IGBTs and conventional EMCBs. It will also describe the differences between IGCTs and IGBTs and will consider the design of the bus voltage snubber. This paper will also show how redesigning the ship's electrical system, while considering these important issues, can result in a more reliable, cost-effective power distribution system for the ship.
Lifelong Machine Learning November , 2016
(2016)

Lifelong machine learning (or lifelong learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to a new height. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
Who are the crowdworkers?: shifting demographics in mechanical turk
(2010)

Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.